在不删除整个ROW的情况下删除NaN'Cells'(Pandas，Python3) [英] Remove NaN 'Cells' without dropping the entire ROW (Pandas,Python3)

问题描述

现在我有一个这样的DF

Right now I have a DF like this

 Word       Word2          Word3
 Hello      NaN            NaN
 My         My Name        NaN
 Yellow     Yellow Bee     Yellow Bee Hive
 Golden     Golden Gates   NaN
 Yellow     NaN            NaN

我希望从数据框中删除所有NaN单元.因此，最终看起来像这样，"Yellow Bee Hive"已移至第1行(类似于在excel中从列中删除单元格时发生的情况):

What I was hoping for was to remove all of the NaN cells from my data frame. So in the end, it would look like this, where 'Yellow Bee Hive' has moved to row 1 (similarly to what happens when you delete cells from a column in excel) :

   Word       Word2             Word3
1  Hello      My Name        Yellow Bee Hive
2  My         Yellow Bee       
3  Yellow     Golden Gates             
4  Golden       
5  Yellow    

不幸的是，这两个都不起作用，因为它们删除了整个行！

Unfortunately, neither of these work because they delete the Entire ROW!

 df = df[pd.notnull(df['Word','Word2','Word3'])]

或

 df = df.dropna() 

有人有什么建议吗?我应该重新索引表格吗?

Anyone have any suggestions? Should I reindex the table?

推荐答案

import numpy as np
import pandas as pd
import functools

def drop_and_roll(col, na_position='last', fillvalue=np.nan):
    result = np.full(len(col), fillvalue, dtype=col.dtype)
    mask = col.notnull()
    N = mask.sum()
    if na_position == 'last':
        result[:N] = col.loc[mask]
    elif na_position == 'first':
        result[-N:] = col.loc[mask]
    else:
        raise ValueError('na_position {!r} unrecognized'.format(na_position))
    return result

df = pd.read_table('data', sep='\s{2,}')

print(df.apply(functools.partial(drop_and_roll, fillvalue='')))

收益

     Word         Word2            Word3
0   Hello       My Name  Yellow Bee Hive
1      My    Yellow Bee                 
2  Yellow  Golden Gates                 
3  Golden                               
4  Yellow     

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